Abstract
The main driver for provenance adoption is the need to collect and understand knowledge about the processes and data that occur in some environment. Before analytical and storage tools can be designed to address this challenge, exemplar data is required both to prototype the analytical techniques and to design infrastructure solutions. Previous attempts to address this requirement have tried to use existing applications as a source; either by collecting data from provenance-enabled applications or by building tools that can extract provenance from the logs of other applications. However, provenance sourced this way can be one-sided, exhibiting only certain patterns, or exhibit correlations or trends present only at the time of collection, and so may be of limited use in other contexts. A better approach is to use a simulator that conforms to explicitly specified domain constraints, and generate provenance data synthetically, replicating the patterns, rules and trends present within the target domain; we describe such a constraint-based simulator here. At the heart of our approach are templates - abstract, reusable provenance patterns within a domain that may be instantiated by concrete substitutions. Domain constraints are configurable and solved using a Constraint Satisfaction Problem solver to produce viable substitutions. Workflows are represented by sequences of templates using probabilistic automata. The simulator is fully integrated within our template-based provenance server architecture, and we illustrate its use in the context of a clinical trials software infrastructure.
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Alper, P., Fairweather, E., Curcin, V. (2018). Simulated Domain-Specific Provenance. In: Belhajjame, K., Gehani, A., Alper, P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science(), vol 11017. Springer, Cham. https://doi.org/10.1007/978-3-319-98379-0_6
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DOI: https://doi.org/10.1007/978-3-319-98379-0_6
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